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  <h1>Source code for torch.nn.parallel.data_parallel</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">operator</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">warnings</span>
<span class="kn">from</span> <span class="nn">itertools</span> <span class="kn">import</span> <span class="n">chain</span>
<span class="kn">from</span> <span class="nn">..modules</span> <span class="kn">import</span> <span class="n">Module</span>
<span class="kn">from</span> <span class="nn">.scatter_gather</span> <span class="kn">import</span> <span class="n">scatter_kwargs</span><span class="p">,</span> <span class="n">gather</span>
<span class="kn">from</span> <span class="nn">.replicate</span> <span class="kn">import</span> <span class="n">replicate</span>
<span class="kn">from</span> <span class="nn">.parallel_apply</span> <span class="kn">import</span> <span class="n">parallel_apply</span>
<span class="kn">from</span> <span class="nn">torch.cuda._utils</span> <span class="kn">import</span> <span class="n">_get_device_index</span>


<span class="k">def</span> <span class="nf">_check_balance</span><span class="p">(</span><span class="n">device_ids</span><span class="p">):</span>
    <span class="n">imbalance_warn</span> <span class="o">=</span> <span class="s2">&quot;&quot;&quot;</span>
<span class="s2">    There is an imbalance between your GPUs. You may want to exclude GPU </span><span class="si">{}</span><span class="s2"> which</span>
<span class="s2">    has less than 75</span><span class="si">% o</span><span class="s2">f the memory or cores of GPU </span><span class="si">{}</span><span class="s2">. You can do so by setting</span>
<span class="s2">    the device_ids argument to DataParallel, or by setting the CUDA_VISIBLE_DEVICES</span>
<span class="s2">    environment variable.&quot;&quot;&quot;</span>
    <span class="n">device_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">_get_device_index</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="kc">True</span><span class="p">),</span> <span class="n">device_ids</span><span class="p">))</span>
    <span class="n">dev_props</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">get_device_properties</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">device_ids</span><span class="p">]</span>

    <span class="k">def</span> <span class="nf">warn_imbalance</span><span class="p">(</span><span class="n">get_prop</span><span class="p">):</span>
        <span class="n">values</span> <span class="o">=</span> <span class="p">[</span><span class="n">get_prop</span><span class="p">(</span><span class="n">props</span><span class="p">)</span> <span class="k">for</span> <span class="n">props</span> <span class="ow">in</span> <span class="n">dev_props</span><span class="p">]</span>
        <span class="n">min_pos</span><span class="p">,</span> <span class="n">min_val</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">values</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">itemgetter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
        <span class="n">max_pos</span><span class="p">,</span> <span class="n">max_val</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">values</span><span class="p">),</span> <span class="n">key</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">itemgetter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
        <span class="k">if</span> <span class="n">min_val</span> <span class="o">/</span> <span class="n">max_val</span> <span class="o">&lt;</span> <span class="mf">0.75</span><span class="p">:</span>
            <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="n">imbalance_warn</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device_ids</span><span class="p">[</span><span class="n">min_pos</span><span class="p">],</span> <span class="n">device_ids</span><span class="p">[</span><span class="n">max_pos</span><span class="p">]))</span>
            <span class="k">return</span> <span class="kc">True</span>
        <span class="k">return</span> <span class="kc">False</span>

    <span class="k">if</span> <span class="n">warn_imbalance</span><span class="p">(</span><span class="k">lambda</span> <span class="n">props</span><span class="p">:</span> <span class="n">props</span><span class="o">.</span><span class="n">total_memory</span><span class="p">):</span>
        <span class="k">return</span>
    <span class="k">if</span> <span class="n">warn_imbalance</span><span class="p">(</span><span class="k">lambda</span> <span class="n">props</span><span class="p">:</span> <span class="n">props</span><span class="o">.</span><span class="n">multi_processor_count</span><span class="p">):</span>
        <span class="k">return</span>


<div class="viewcode-block" id="DataParallel"><a class="viewcode-back" href="../../../../nn.html#torch.nn.DataParallel">[docs]</a><span class="k">class</span> <span class="nc">DataParallel</span><span class="p">(</span><span class="n">Module</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Implements data parallelism at the module level.</span>

<span class="sd">    This container parallelizes the application of the given :attr:`module` by</span>
<span class="sd">    splitting the input across the specified devices by chunking in the batch</span>
<span class="sd">    dimension (other objects will be copied once per device). In the forward</span>
<span class="sd">    pass, the module is replicated on each device, and each replica handles a</span>
<span class="sd">    portion of the input. During the backwards pass, gradients from each replica</span>
<span class="sd">    are summed into the original module.</span>

<span class="sd">    The batch size should be larger than the number of GPUs used.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        It is recommended to use :class:`~torch.nn.parallel.DistributedDataParallel`,</span>
<span class="sd">        instead of this class, to do multi-GPU training, even if there is only a single</span>
<span class="sd">        node. See: :ref:`cuda-nn-ddp-instead` and :ref:`ddp`.</span>

<span class="sd">    Arbitrary positional and keyword inputs are allowed to be passed into</span>
<span class="sd">    DataParallel but some types are specially handled. tensors will be</span>
<span class="sd">    **scattered** on dim specified (default 0). tuple, list and dict types will</span>
<span class="sd">    be shallow copied. The other types will be shared among different threads</span>
<span class="sd">    and can be corrupted if written to in the model&#39;s forward pass.</span>

<span class="sd">    The parallelized :attr:`module` must have its parameters and buffers on</span>
<span class="sd">    ``device_ids[0]`` before running this :class:`~torch.nn.DataParallel`</span>
<span class="sd">    module.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        In each forward, :attr:`module` is **replicated** on each device, so any</span>
<span class="sd">        updates to the running module in ``forward`` will be lost. For example,</span>
<span class="sd">        if :attr:`module` has a counter attribute that is incremented in each</span>
<span class="sd">        ``forward``, it will always stay at the initial value because the update</span>
<span class="sd">        is done on the replicas which are destroyed after ``forward``. However,</span>
<span class="sd">        :class:`~torch.nn.DataParallel` guarantees that the replica on</span>
<span class="sd">        ``device[0]`` will have its parameters and buffers sharing storage with</span>
<span class="sd">        the base parallelized :attr:`module`. So **in-place** updates to the</span>
<span class="sd">        parameters or buffers on ``device[0]`` will be recorded. E.g.,</span>
<span class="sd">        :class:`~torch.nn.BatchNorm2d` and :func:`~torch.nn.utils.spectral_norm`</span>
<span class="sd">        rely on this behavior to update the buffers.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        Forward and backward hooks defined on :attr:`module` and its submodules</span>
<span class="sd">        will be invoked ``len(device_ids)`` times, each with inputs located on</span>
<span class="sd">        a particular device. Particularly, the hooks are only guaranteed to be</span>
<span class="sd">        executed in correct order with respect to operations on corresponding</span>
<span class="sd">        devices. For example, it is not guaranteed that hooks set via</span>
<span class="sd">        :meth:`~torch.nn.Module.register_forward_pre_hook` be executed before</span>
<span class="sd">        `all` ``len(device_ids)`` :meth:`~torch.nn.Module.forward` calls, but</span>
<span class="sd">        that each such hook be executed before the corresponding</span>
<span class="sd">        :meth:`~torch.nn.Module.forward` call of that device.</span>

<span class="sd">    .. warning::</span>
<span class="sd">        When :attr:`module` returns a scalar (i.e., 0-dimensional tensor) in</span>
<span class="sd">        :func:`forward`, this wrapper will return a vector of length equal to</span>
<span class="sd">        number of devices used in data parallelism, containing the result from</span>
<span class="sd">        each device.</span>

<span class="sd">    .. note::</span>
<span class="sd">        There is a subtlety in using the</span>
<span class="sd">        ``pack sequence -&gt; recurrent network -&gt; unpack sequence`` pattern in a</span>
<span class="sd">        :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`.</span>
<span class="sd">        See :ref:`pack-rnn-unpack-with-data-parallelism` section in FAQ for</span>
<span class="sd">        details.</span>


<span class="sd">    Args:</span>
<span class="sd">        module (Module): module to be parallelized</span>
<span class="sd">        device_ids (list of int or torch.device): CUDA devices (default: all devices)</span>
<span class="sd">        output_device (int or torch.device): device location of output (default: device_ids[0])</span>

<span class="sd">    Attributes:</span>
<span class="sd">        module (Module): the module to be parallelized</span>

<span class="sd">    Example::</span>

<span class="sd">        &gt;&gt;&gt; net = torch.nn.DataParallel(model, device_ids=[0, 1, 2])</span>
<span class="sd">        &gt;&gt;&gt; output = net(input_var)  # input_var can be on any device, including CPU</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="c1"># TODO: update notes/cuda.rst when this class handles 8+ GPUs well</span>

    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">DataParallel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>

        <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">():</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">module</span> <span class="o">=</span> <span class="n">module</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span> <span class="o">=</span> <span class="p">[]</span>
            <span class="k">return</span>

        <span class="k">if</span> <span class="n">device_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">device_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device_count</span><span class="p">()))</span>
        <span class="k">if</span> <span class="n">output_device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
            <span class="n">output_device</span> <span class="o">=</span> <span class="n">device_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">dim</span> <span class="o">=</span> <span class="n">dim</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">module</span> <span class="o">=</span> <span class="n">module</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">_get_device_index</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="kc">True</span><span class="p">),</span> <span class="n">device_ids</span><span class="p">))</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">output_device</span> <span class="o">=</span> <span class="n">_get_device_index</span><span class="p">(</span><span class="n">output_device</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">src_device_obj</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>

        <span class="n">_check_balance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">)</span>

        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">cuda</span><span class="p">(</span><span class="n">device_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>

        <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">chain</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="o">.</span><span class="n">buffers</span><span class="p">()):</span>
            <span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">device</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">src_device_obj</span><span class="p">:</span>
                <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;module must have its parameters and buffers &quot;</span>
                                   <span class="s2">&quot;on device </span><span class="si">{}</span><span class="s2"> (device_ids[0]) but found one of &quot;</span>
                                   <span class="s2">&quot;them on device: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">src_device_obj</span><span class="p">,</span> <span class="n">t</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>

        <span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
        <span class="n">replicas</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">replicate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">module</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)])</span>
        <span class="n">outputs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">parallel_apply</span><span class="p">(</span><span class="n">replicas</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_device</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">replicate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">,</span> <span class="n">device_ids</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">replicate</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">device_ids</span><span class="p">,</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">is_grad_enabled</span><span class="p">())</span>

    <span class="k">def</span> <span class="nf">scatter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">device_ids</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">scatter_kwargs</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">device_ids</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">parallel_apply</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">replicas</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">parallel_apply</span><span class="p">(</span><span class="n">replicas</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">device_ids</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">replicas</span><span class="p">)])</span>

    <span class="k">def</span> <span class="nf">gather</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">):</span>
        <span class="k">return</span> <span class="n">gather</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dim</span><span class="p">)</span></div>


<div class="viewcode-block" id="data_parallel"><a class="viewcode-back" href="../../../../nn.functional.html#torch.nn.data_parallel">[docs]</a><span class="k">def</span> <span class="nf">data_parallel</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">device_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_device</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">module_kwargs</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
    <span class="sa">r</span><span class="sd">&quot;&quot;&quot;Evaluates module(input) in parallel across the GPUs given in device_ids.</span>

<span class="sd">    This is the functional version of the DataParallel module.</span>

<span class="sd">    Args:</span>
<span class="sd">        module (Module): the module to evaluate in parallel</span>
<span class="sd">        inputs (Tensor): inputs to the module</span>
<span class="sd">        device_ids (list of int or torch.device): GPU ids on which to replicate module</span>
<span class="sd">        output_device (list of int or torch.device): GPU location of the output  Use -1 to indicate the CPU.</span>
<span class="sd">            (default: device_ids[0])</span>
<span class="sd">    Returns:</span>
<span class="sd">        a Tensor containing the result of module(input) located on</span>
<span class="sd">        output_device</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
        <span class="n">inputs</span> <span class="o">=</span> <span class="p">(</span><span class="n">inputs</span><span class="p">,)</span>

    <span class="k">if</span> <span class="n">device_ids</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">device_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">device_count</span><span class="p">()))</span>

    <span class="k">if</span> <span class="n">output_device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
        <span class="n">output_device</span> <span class="o">=</span> <span class="n">device_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>

    <span class="n">device_ids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">_get_device_index</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="kc">True</span><span class="p">),</span> <span class="n">device_ids</span><span class="p">))</span>
    <span class="n">output_device</span> <span class="o">=</span> <span class="n">_get_device_index</span><span class="p">(</span><span class="n">output_device</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
    <span class="n">src_device_obj</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">device_ids</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>

    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">chain</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">module</span><span class="o">.</span><span class="n">buffers</span><span class="p">()):</span>
        <span class="k">if</span> <span class="n">t</span><span class="o">.</span><span class="n">device</span> <span class="o">!=</span> <span class="n">src_device_obj</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">RuntimeError</span><span class="p">(</span><span class="s2">&quot;module must have its parameters and buffers &quot;</span>
                               <span class="s2">&quot;on device </span><span class="si">{}</span><span class="s2"> (device_ids[0]) but found one of &quot;</span>
                               <span class="s2">&quot;them on device: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">src_device_obj</span><span class="p">,</span> <span class="n">t</span><span class="o">.</span><span class="n">device</span><span class="p">))</span>

    <span class="n">inputs</span><span class="p">,</span> <span class="n">module_kwargs</span> <span class="o">=</span> <span class="n">scatter_kwargs</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">module_kwargs</span><span class="p">,</span> <span class="n">device_ids</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">device_ids</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
        <span class="k">return</span> <span class="n">module</span><span class="p">(</span><span class="o">*</span><span class="n">inputs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">**</span><span class="n">module_kwargs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">used_device_ids</span> <span class="o">=</span> <span class="n">device_ids</span><span class="p">[:</span><span class="nb">len</span><span class="p">(</span><span class="n">inputs</span><span class="p">)]</span>
    <span class="n">replicas</span> <span class="o">=</span> <span class="n">replicate</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">used_device_ids</span><span class="p">)</span>
    <span class="n">outputs</span> <span class="o">=</span> <span class="n">parallel_apply</span><span class="p">(</span><span class="n">replicas</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">module_kwargs</span><span class="p">,</span> <span class="n">used_device_ids</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">gather</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">output_device</span><span class="p">,</span> <span class="n">dim</span><span class="p">)</span></div>
</pre></div>

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